update trainer

ppcls/engine/trainer.py

@@ -158,7 +158,6 @@ class Trainer(object):
         for epoch_id in range(best_metric["epoch"] + 1,
                               self.config["Global"]["epochs"] + 1):
             acc = 0.0
-            self.model.train()
             for iter_id, batch in enumerate(self.train_dataloader()):
                 batch_size = batch[0].shape[0]
                 batch[1] = paddle.to_tensor(batch[1].numpy().astype("int64")
@@ -241,34 +240,34 @@ class Trainer(object):
 
     @paddle.no_grad()
     def eval(self, epoch_id=0):
+        self.model.eval()
+        if self.eval_mode == "classification":
             if self.eval_dataloader is None:
-            self.eval_dataloader = build_dataloader(self.config["DataLoader"],
-                                                    "Eval", self.device)
+                self.eval_dataloader = build_dataloader(
+                    self.config["DataLoader"], "Eval", self.device)
+            eval_result = self.eval_cls(epoch_id)
+
+        elif self.eval_mode == "retrieval":
             if self.gallery_dataloader is None:
                 self.gallery_dataloader = build_dataloader(
                     self.config["DataLoader"], "Gallery", self.device)
 
             if self.query_dataloader is None:
-            self.query_dataloader = build_dataloader(self.config["DataLoader"],
-                                                     "Query", self.device)
-
+                self.query_dataloader = build_dataloader(
+                    self.config["DataLoader"], "Query", self.device)
             # build train loss and metric info
             if self.eval_loss_func is None:
-            self.eval_loss_func = self._build_loss_info(self.config["Loss"],
-                                                        "eval")
-
+                self.eval_loss_func = self._build_loss_info(
+                    self.config["Loss"], "eval")
             if self.eval_metric_func is None:
                 self.eval_metric_func = self._build_metric_info(
                     self.config["Metric"], "eval")
-
-        self.model.eval()
-
-        if self.eval_mode == "classification":
-            self.eval_cls(epoch_id)
-        elif self.eval_mode == "retrieval":
-            self.eval_retrieval(epoch_id)
+            eval_result = self.eval_retrieval(epoch_id)
         else:
             logger.warning("Invalid eval mode: {}".format(self.eval_mode))
+            eval_result = None
+        self.model.train()
+        return eval_result
 
     def eval_cls(self, epoch_id=0):
         output_info = dict()
@@ -332,9 +331,8 @@ class Trainer(object):
         return output_info[metric_key].avg
 
     def eval_retrieval(self, epoch_id=0):
-        output_info = dict()
         self.model.eval()
-
+        cum_similarity_matrix = None
         # step1. build gallery
         gallery_feas, gallery_img_id, gallery_camera_id = self._cal_feature(
             name='gallery')
@@ -342,7 +340,7 @@ class Trainer(object):
             name='query')
         gallery_img_id = paddle.to_tensor([gallery_img_id]).t()
         if gallery_camera_id is not None:
-            gallery_camera_id = paddle.to_tensor(gallery_camera_id).t()
+            gallery_camera_id = paddle.to_tensor([gallery_camera_id]).t()
         query_img_id = paddle.to_tensor(query_img_id)
         if query_camera_id is not None:
             query_camera_id = paddle.to_tensor(query_camera_id)
@@ -352,35 +350,37 @@ class Trainer(object):
         if not len(query_feas) % sim_block_size:
             sections.append(len(query_feas) % sim_block_size)
         fea_blocks = paddle.split(query_feas, num_or_sections=sections)
+        if query_camera_id is not None:
             camera_id_blocks = paddle.split(
                 query_camera_id, num_or_sections=sections)
         image_id_blocks = paddle.split(query_img_id, num_or_sections=sections)
         metric_key = None
 
         for block_idx, block_fea in enumerate(fea_blocks):
-            similarities_matrix = paddle.matmul(
+            similarity_matrix = paddle.matmul(
                 block_fea, gallery_feas, transpose_y=True)
             image_id_block = image_id_blocks[block_idx]
-            image_id_mask = (image_id_block == gallery_img_id)
-            similarities_matrix = similarities_matrix.masked_select(
-                image_id_mask)
+            image_id_mask = (image_id_block != gallery_img_id)
+            similarity_matrix = similarity_matrix.masked_select(image_id_mask)
+            if query_camera_id is not None:
                 camera_id_block = camera_id_blocks[block_idx]
-            camera_id_mask = (camera_id_block == gallery_camera_id)
-            similarities_matrix = similarities_matrix.masked_select(
+                camera_id_mask = (camera_id_block != gallery_camera_id)
+                similarity_matrix = similarity_matrix.masked_select(
                     camera_id_mask)
+            if similarity_matrix is None:
+                cum_similarity_matrix = similarity_matrix
+            else:
+                cum_similarity_matrix = paddle.concat(cum_similarity_matrix,
+                                                      similarity_matrix)
 
         # calc metric
         if self.eval_metric_func is not None:
-                metric_dict = self.eval_metric_func(similarities_matrix,
-                                                    image_id_block)
-                for key in metric_dict:
-                    if metric_key is None:
-                        metric_key = key
-                    if not key in output_info:
-                        output_info[key] = AverageMeter(key, '7.5f')
+            metric_dict = self.eval_metric_func(cum_similarity_matrix,
+                                                query_img_id, gallery_img_id)
+        else:
+            metric_dict = {metric_key: 0.}
 
-                    output_info[key].update(metric_dict[key].numpy()[0],
-                                            len(image_id_block))
+        return metric_dict[metric_key]
 
     def _cal_feature(self, name='gallery'):
         all_feas = None